Menopause

Artificial Intelligence-based analysis of pre-eclampsia gene expression profiles for identification of novel potential pre-eclampsia diagnostic biomarkers.

Preeclampsia (PE) is a multifactorial and heterogeneous hypertensive disorder of pregnancy that poses significant diagnostic and therapeutic challenges. Identifying robust and generalizable biomarkers is critical for early detection and improved clinical outcomes. In this study, we present a machine learning-based framework for the analysis of gene expression profiles to uncover potential diagnost...

Association of epicardial adipose tissue with markers of cardiac remodelling and clinical outcomes in asymptomatic aortic stenosis.

BACKGROUND: Previous studies have suggested an adverse role of epicardial adipose tissue (EAT) in aortic stenosis (AS), potentially mediated by direct effects on the myocardium. This study aimed to assess whether EAT volume and density are associated with cardiac remodelling and clinical outcomes in a well-phenotyped cohort of patients with initially asymptomatic AS. METHODS: In this post hoc expl...

High-Resolution MRI Using Artificial Intelligence-Assisted Acceleration and Radial Dynamic Contrast Enhancement for Improved Detection of Pituitary Microadenomas in Cushing's Disease.

BACKGROUND AND PURPOSE: Accurate detection of pituitary microadenomas is critical for the diagnosis and treatment of Cushing's disease (CD). However, ...

Profiling reasons for non-attendance in psychological assessments in adolescent suicide at-risk group using natural language processing.

BACKGROUND: Non-attendance at follow-up psychological evaluations is a major barrier to care for adolescents at risk of suicide. Identifying the facto...

Opportunistic osteoporosis screening using CT-based Hounsfield units: a machine learning approach for preoperative spine surgery planning.

Attitudes toward AI in health- and non-health-related contexts: Context matters, not mood or perceived social support.

As artificial intelligence (AI) systems increasingly permeate healthcare and daily life, understanding the factors that shape attitudes toward their u...

Wearable electrochemical biosensors for non-invasive sport doping control: sweat and saliva as dual biofluid platforms.

The integrity of competitive athletics is increasingly compromised by sophisticated pharmacologies, rendering traditional sporadic testing of urine an...

Open-Source Machine Learning Computed Tomography Scan Segmentation for Spine Osteoporosis Diagnostics.

BACKGROUND AND OBJECTIVES: Dual-energy x-ray absorptiometry (DXA) is the standard for assessing bone mineral density (BMD); however, its accuracy is l...

Auto-LSN: fully automated liver surface nodularity quantification in CT based on deep learning for the evaluation of advanced chronic liver disease.

OBJECTIVES: Liver surface nodularity (LSN) is a recognized non-invasive biomarker of cirrhosis. This study introduces auto-LSN, an artificial intellig...

Evaluation of deliverable dose-mimicking automated volumetric arc radiation therapy planning for stage III non-small cell lung cancer patients: comparison with a commercial DVH-predicted automated planning system.

This study aimed to evaluate the clinical validity of a dose-mimicking automated planning for volumetric-modulated arc therapy (VMAT) in patients with...

Ethical Risks and Structural Implications of AI-Mediated Medical Interpreting.

Artificial intelligence (AI) is increasingly used to support medical interpreting and public health communication, yet current systems introduce serio...

Impacts of delayed influenza vaccination on clinical outcomes in ICU-admitted patients with influenza: A retrospective cohort study.

BACKGROUND: Influenza vaccination is an effective measure for reducing the risk of severe influenza infection. However, the task of achieving adequate...

Association of urinary heavy metals with osteoporosis in US adults using interpretable machine learning.

BACKGROUND: Exposure to heavy metals in the environment has always been the focus of public concern. More and more evidence suggests that heavy metal ...

Machine learning-enabled meta-analysis reveals the effect of microplastics on nitrogen removal performance in constructed wetlands and its potential mechanisms.

Constructed wetlands (CWs) are an effective wastewater treatment system and an important sink for microplastics (MPs). However, MPs impacts on nitroge...

Fracture risk scores using output from an opportunistic screen of low bone density from conventional X-ray.

Fracture risk is commonly assessed by FRAX, a tool that estimates 10-yr risk for major osteoporotic fracture (MOF) and hip fracture. FRAX scores are o...

Near-infrared spectroscopy and machine-learning evaluation of PVA/β-TCP biomaterial composites.

A composite of polyvinyl alcohol (PVA) and beta-tricalcium phosphate (β-TCP) was synthesized as a biomaterial filament for 3D printers and its analyti...

Circulating tumor DNA dynamics predict pathological response and guide therapy personalization in the neoadjuvant setting.

Neoadjuvant therapy is a cornerstone of modern oncology, yet its efficacy is traditionally assessed only after treatment completion, creating a "black...

Osteoporosis prediction using lumbar CT Hounsfield units: comparative performance and clinical implications of seven machine learning models.

PURPOSE: This study aimed to evaluate the utility of L1-L4 average Hounsfield Unit (HU) values from lumbar spine computed tomography (CT) in predictin...

Lipid monitoring using non-invasive measurement technologies and machine learning: a systematic review.

BACKGROUND: Cardiovascular diseases (CVD) are the leading cause of death among women, with risk increasing after menopause. Lipid levels are key bioma...

Padé Neurons for Efficient Neural Models.

Neural networks commonly employ the McCulloch-Pitts neuron model, which is a linear model followed by a point-wise non-linear activation. Various rese...